This document contains the description for the PV portfolio optimizer. In the first stage, this optimizer will clasify the PV systems within a PV portfolio into 3 main categories:
In this example 1 day man-labor cost = 1000 CHF and price per kWh = 0.6296 CHF.
NOTE: The icons for this optimizer have been created with this website.
The map will be started based on this initial location. In this case, we use Geneve as initial location city as follows:
Get location to initialize the map
city = "Geneve"
get location
locator = geopy.geocoders.Nominatim(user_agent="MyCoder")
location = locator.geocode(city)
print('Location:' location)
keep latitude and longitude only
location = [location.latitude, location.longitude]
print("[lat, long]:", location)
Location: Genève, 1204, Schweiz/Suisse/Svizzera/Svizra [lat, long]: [46.2017559, 6.1466014]
The accumulated losses for SIG PV plants portfolio looks as follows:
As mentioned before:
The clustering of the PV systems is based on K-Means algorithm from sklearn library, with a twist. Basically what we do is the following:
This cycle is repeated until the optimum number of clusters are defined. Same color circles indicate same cluster.